Multiuser detection (“MUD”) is an effective approach for detecting multiple, simultaneous data streams transmitted by a plurality of frequency sources (generically referred to herein as “users”) on a common frequency channel, thereby providing a significant increase in the spectral efficiency of the communication network. Applications include, but are not limited to, cellular telephone communication of voice and data to and from cellular handsets, other wireless mobile devices, and wireless base stations.
Several methods can be used to perform multiuser detection, including minimum mean-square error (MMSE), zero forcing (ZF), maximum-likelihood detection (MLD), and such like. MLD provides the best performance, but at the cost of very high implementation complexity. Therefore, less complex but close to optimal non-linear successive interference cancellation (SIC) based receiver architectures involving turbo loops have recently become very attractive.
In a turbo looped architecture, so as to further reduce complexity, linear MMSE-IRC (interference rejection combining) equalization is generally performed on data streams in successive turbo loops. However, as the dimensions of equalization increase, due for example to an increase in the number of users and/or the number of antennas used in a MIMO multiuser system, the computational complexity increases at a rate that is more than linear.
Most of the computational complexity of a linear MMSE equalization is associated with computing the inverse of the received signal's covariance matrix. Several efficient numerical methods of obtaining the solution of a linear system can be found in the literature, such as QR decomposition, Gauss-Jordan elimination, Cholesky decomposition, and such like. Among these methods, QR decomposition followed by backward and forward substitution has been shown to be robust in achieving a high accuracy solution without any need to compute the covariance matrix and without performing an actual matrix inversion. However, even when implementing QR decomposition there can be significant, potentially impractical computational overhead (in terms of MIPS) when implementing a turbo loop based approach, even for a moderately large MIMO multiuser system.
It is therefore essential for meeting standard product release criteria to develop methods that reduce the computational complexity of a turbo-looped implementation, while retaining near-optimal turbo-loop performance.
What is needed, therefore, is an improved receiver design for modeling users in SIC turbo loop multiuser detection architectures that reduces the computational complexity of the turbo-looped implementation while retaining near-optimal turbo-loop performance.